def resize(self, shape, electrodes=None):
"""Resize the video
Parameters
----------
shape : (rows, cols)
Shape of each frame in the resized video. If one of the dimensions
is set to -1, its value will be inferred by keeping a constant
aspect ratio.
electrodes : int, string or list thereof; optional
Optionally, you can provide your own electrode names. If none are
given, electrode names will be numbered 0..N.
.. note::
The number of electrode names provided must match the number of
pixels in the resized video.
Returns
-------
stim : `VideoStimulus`
A copy of the stimulus object containing the resized video
"""
height, width = shape
if height < 0 and width < 0:
raise ValueError('"height" and "width" cannot both be -1.')
if height < 0:
height = int(self.vid_shape[0] * width / self.vid_shape[1])
if width < 0:
width = int(self.vid_shape[1] * height / self.vid_shape[0])
vid = vid_resize(self.data.reshape(self.vid_shape),
(height, width, *self.vid_shape[2:]))
return VideoStimulus(vid, electrodes=electrodes, time=self.time,
metadata=self.metadata)
def __init__(self,
source,
format=None,
resize=None,
as_gray=False,
electrodes=None,
time=None,
metadata=None,
compress=False):
if metadata is None:
metadata = {}
elif type(metadata) != dict:
metadata = {'user': metadata}
if isinstance(source, str):
# Filename provided, read the video:
reader = video_reader(source, format=format)
vid = np.array([frame for frame in reader])
# Move frame index to the last dimension:
if vid.ndim == 4:
vid = vid.transpose((1, 2, 3, 0))
elif vid.ndim == 3:
vid = vid.transpose((1, 2, 0))
# Combine video metadata with user-specified metadata:
meta = reader.get_meta_data()
if meta is not None:
metadata.update(meta)
metadata['source'] = source
metadata['source_shape'] = vid.shape
# Infer the time points from the video frame rate:
time = np.arange(vid.shape[-1]) * meta['fps']
elif isinstance(source, VideoStimulus):
vid = source.data.reshape(source.vid_shape)
metadata.update(source.metadata)
if electrodes is None:
electrodes = source.electrodes
if time is None:
time = source.time
elif isinstance(source, np.ndarray):
vid = source
else:
raise TypeError("Source must be a filename, a 3D NumPy array or "
"another VideoStimulus, not %s." % type(source))
if vid.ndim < 3 or vid.ndim > 4:
raise ValueError("Videos must have 3 or 4 dimensions, not "
"%d." % vid.ndim)
# Convert to grayscale if necessary:
if as_gray:
if vid.ndim == 4:
vid = rgb2gray(vid.transpose((0, 1, 3, 2)))
# Resize if necessary:
if resize is not None:
height, width = resize
if height < 0 and width < 0:
raise ValueError('"height" and "width" cannot both be -1.')
if height < 0:
height = int(vid.shape[0] * width / vid.shape[1])
if width < 0:
width = int(vid.shape[1] * height / vid.shape[0])
vid = vid_resize(vid, (height, width, *vid.shape[2:]))
# Store the original image shape for resizing and color conversion:
self.vid_shape = vid.shape
# Convert to float array in [0, 1] and call the Stimulus constructor:
vid = img_as_float32(vid)
super(VideoStimulus, self).__init__(vid.reshape((-1, vid.shape[-1])),
time=time,
electrodes=electrodes,
metadata=metadata,
compress=compress)
self.metadata = metadata
self.rewind()